The United States electrical power supply system consists of multiple electrical power generation stations and electrical power users (loads) all connected to an electrical power system generally referred to as the “grid.” Control of that system is typically provided by manual and automated systems within a group of electrical power providers, each of which in the United States is generally called an Electrical Utility or Co-Op. These power control systems offer many advantages and disadvantages.
With the increasing penetration of intermittent renewable energy, power systems encounter more and more uncertainty and variability. How to reliably and efficiently operate a power system in such an environment is still an unanswered challenging question. With state-of-the-art wind forecasting methods, the hour-ahead forecast errors for a single wind plant are still around 10%-15% with respect to its actual outputs. With much lower forecasting errors for loads, the traditional power system operation is based on deterministic security-constrained commitment and dispatch processes, which tend to be conservative (using forecasts with a high probability of exceedance) when intermittent renewable generation is considered. This conservative operation contributes to a large amount of wind curtailment, as secure operation cannot be guaranteed in real time when the actual wind power significantly exceeds the forecasts used in the scheduling and dispatching processes.
The optimal power flow (OPF), or its security-constrained version, is based on steady-state optimization without considering local controller and load dynamics, and its optimal solutions are obtained based on forecasts. With uncertainty from renewable generation and storage, the convexity of the OPF problem is often the subject of research. Although the OPF provides optimal dispatches for the next forecasted period, any unforeseen real-time load/generation variation or post-contingency operation between two dispatch instants (typically 5 minutes apart) are handled by simple linear controllers or some predefined reactions with little, if any, system-wide optimization. For real-time active power balancing, the proportional-integral-controller-based automatic generation control (AGC) is typically used. For reactive power support, locally-controlled reactive resources are typically used for voltage regulation, such as large generators equipped with automatic voltage regulators (AVRs), switched capacitor banks, on-load tap changing (OLTC) transformers, and flexible AC transmission system (FACTS) devices. Moreover, the variety of active power generation controllers and reactive power generation controllers is quite broad so that a power control system can output control data to a vast array of devices and systems to vary the amount of active and reactive power within a power system.
The development of wide-area measurement systems (WAMSs), based on synchronized phasor measurement units (PMUs), greatly improves the power grid observability, even during transient events. WAMSs enable distributed dynamic state estimation, which can dramatically reduce the reporting time of the global system states (from minutes down to fractions of a second) and improve the grid visibility from steady states to dynamic behaviors. With the global dynamic information, advanced wide-area control (WAC) schemes become possible to improve grid dynamics. Most of the WAC schemes to date have focused on power system stability related issues, including the transient/small-signal stabilizing control to mitigate angle instability, and the secondary voltage control to mitigate voltage instability. The design of a system-wide automatic power flow controller to dynamically control a power system to its optimal operating point has received little attention.
B. Fardanesh described an ideal control scenario for power systems, where the optimal operating condition was achieved continuously by some closed-loop control algorithms, but he did not describe how to design such a control algorithm. See B. Fardanesh, “Future Trends in Power System Control,” IEEE Comput. Appl. Power, vol. 15, no. 3, pp. 24-31, July 2002. Conceptual frameworks for applying adaptive critic designs (ACDs) to power system optimizations, namely dynamic stochastic optimizations, have also been proposed. No one has yet described any detailed designs or analyses for a power system control, however, to incorporate prediction and optimization over power system stochastic disturbances.
Further to this point, existing power system active and reactive power control methods for the grid, including automatic generation control and regional voltage control, are based on linear proportional-integral controllers. These linear controllers cannot consider multiple control objectives and cannot ensure system security in real-time. To achieve a high penetration level of intermittent renewable energy (e.g., wind, photovoltaic, and solar thermal) generation, the control of power systems needs to account for the high short-term variability and uncertainty associated with these intermittent energy sources. Power system security needs to be ensured dynamically as the system operating condition continuously changes.
It is important to understand that generators inject different amounts of active and reactive power into the power system. FACTS devices generate reactive power, but can also regulate the flow of active and reactive power in the grid.
Moreover, the existing structure of power system operation and control is organized in three layers: the primary, secondary, and tertiary control layers respectively. The primary control consists of controls at the local generator and device levels, and has no visibility into the rest of the power system. The secondary control consists of controls for an area power network. These controls include automatic generation control (AGC) for regulating the system frequency, and the regional voltage control (RVC) for regulating voltages of certain buses within the area. The tertiary control layer, which is slower than the secondary control layer, is typically based on a steady-state OPF algorithm that minimizes the steady-state overall system operation cost of one or more areas. Lines that interconnect different areas are known as tie lines.
The tertiary control sends steady-state or set-point commands, which are obtained based on forecasts, to generators and FACTS devices typically every 5 minutes. Any unforeseen real-time load and/or generation variations or system topology changes due to grid contingencies need to be handled by the secondary controllers. The secondary controls react to disturbances in power systems and adjust the steady-state commands at intervals of 1 to 4 seconds.
For the secondary active power control, the system frequency and inter-area tie-line flows are regulated by the AGC, which is typically a simple proportional-integral (PI) controller. The AGC treats the grid as a single bus (or node) and does not consider system constraints (e.g., line loadings, bus voltages). For the secondary reactive power control, a power system is typically divided into separate voltage regulation regions with each region having its own pilot bus. The main generators in each region are used to regulate the voltage of the pilot bus by using a linear PI controller. No coordination between the secondary active and reactive power controls has been developed and reported in the prior art.
The existing linear secondary control schemes for frequency and voltage are based on the assumption that only small variations and uncertainties exist in power systems during a short period of time. With high penetration of intermittent renewable energy, significant power flow redistribution may occur in a short period of time. A more sophisticated coordinating control method is needed to ensure real-time system security.
What is needed is a dynamic stochastic optimal power flow (DSOPF) control system that provides multi-objective optimal control capability to complex electrical power systems. It would be beneficial if the DSOPF control may be implemented using nonlinear optimal control techniques, including but not limited to adaptive critic designs and the model predictive control method. Put in other terms, it would be beneficial if the system and method could provide a coordinated secondary AC power flow control solution to electrical power systems with high penetration of intermittent renewable energy generation.
An object of the present invention is to provide a dynamic stochastic optimal power flow (DSOPF) control scheme to provide multi-objective optimal control capability to complex electrical power systems.
A further object of the invention is to implement the DSOPF control using nonlinear optimal control techniques, including but not limited to adaptive critic designs and the model predictive control method.
Yet another object of the invention is to provide a coordinated secondary AC power flow control solution to electrical power systems with high penetration of intermittent renewable electrical energy generation.